Altered prefrontal activation during the inhibition of eating responses in women with bulimia nervosa.

BACKGROUND: The sense of 'loss of control' (LOC), or a feeling of being unable to stop eating or control what or how much one is eating, is the most salient aspect of binge eating. However, the neural alterations that may contribute to this experience and eating behavior remain poorly understood. METHODS: We used functional near-infrared spectroscopy (fNIRS) to measure activation in the prefrontal cortices of 23 women with bulimia nervosa (BN) and 23 healthy controls (HC) during two tasks: a novel go/no-go task requiring inhibition of eating responses, and a standard go/no-go task requiring inhibition of button-pressing responses. RESULTS: Women with BN made more commission errors on both tasks. BN subgroups with the most severe LOC eating (n = 12) and those who felt most strongly that they binge ate during the task (n = 12) showed abnormally reduced bilateral ventromedial prefrontal cortex (vmPFC) and right ventrolateral prefrontal cortex (vlPFC) activation associated with eating-response inhibition. In the entire BN sample, lower eating-task activation in right vlPFC was related to more frequent and severe LOC eating, but no group differences in activation were detected on either task when this full sample was compared with HC. BN severity was unrelated to standard-task activation. CONCLUSIONS: Results provide initial evidence that diminished PFC activation may directly contribute to more severe eating-specific control deficits in BN. Our findings support vmPFC and vlPFC dysfunction as promising treatment targets, and indicate that eating-specific tasks and fNIRS may be useful tools for identifying neural mechanisms underlying dysregulated eating.

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies.

Importance: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. Objective: To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. Design, Setting, and Participants: This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. Exposure: An AI-based assistive tool for Gleason grading of prostate biopsies. Main Outcomes and Measures: Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. Results: Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. Conclusions and Relevance: In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

Implicit Mental Motor Imagery Task Demonstrates a Distortion of the Body Schema in Patients With Eating Disorders.

OBJECTIVES: A rich body of literature has established the role of body image distortion and dissatisfaction in the development and maintenance of eating disorders. However, many of the currently used techniques require explicit comparison of the person's body to an external stimulus. As the body schema is a largely unconscious construct, explicit comparison tasks may reflect a proxy, rather than the body schema itself. METHODS: Here we use an implicit mental motor imagery (MMI) task to interrogate the body schema in healthy control participants (N=40) and participants at a residential eating disorder treatment center (N=42). By comparing the time it takes to imagine making a movement along a part of the body to the time it takes to actually make the same movement, we were able to assess participants' mental image of their body (i.e., body schema). RESULTS: We found that participants with eating disorders, but not healthy controls, exhibited distortions of the body schema such that they believed their abdomen, buttocks, and thighs to be larger than they really are. Additionally, the MMI task used here provided information above and beyond traditional self-report measures (i.e., Body Shape Questionnaire). Together the MMI task and traditional measures provide the most information. CONCLUSIONS: Findings using the novel MMI task are in line with the literature; participants with eating disorders consider themselves to be larger than they truly are. Taken together, results of this study suggest that MMI tasks provide complementary information to traditional self-report measures. (JINS, 2018, 24, 715-723).

The role of hunger state and dieting history in neural response to food cues: An event-related potential study.

A history of dieting to lose weight has been shown to be a robust predictor of future weight gain. A potential factor in propensity towards weight gain is the nature of people's reactions to the abundance of highly palatable food cues in the environment. Event Related Potentials (ERPs) have revealed differences in how the brain processes food cues between obese and normal weight individuals, as well as between restrained and unrestrained eaters. However, comparisons by weight status are not informative regarding whether differences predate or follow weight gain in obese individuals and restrained eating has not consistently been found to predict future weight gain. The present study compared ERP responses to food cues in non-obese historic dieters (HDs) to non-obese never dieters (NDs). HDs showed a blunted N1 component relative to NDs overall, and delayed N1 and P2 components compared to NDs in the hungry state, suggesting that early, perceptual processing of food cues differs between these groups, especially when food-deprived. HDs also showed a more hunger-dependent sustained ERP (LPP) compared to NDs. Future research should test ERP-based food cue responsivity as a mediator between dieting history and future weight gain to better identify those most at risk for weight gain as well as the nature of their vulnerability.

Behind binge eating: A review of food-specific adaptations of neurocognitive and neuroimaging tasks.

Recurrent binge eating, or overeating accompanied by a sense of loss of control, is a major public health concern. Identifying similarities and differences among individuals with binge eating and those with other psychiatric symptoms and characterizing the deficits that uniquely predispose individuals to eating problems are essential to improving treatment. Research suggests that altered reward and control-related processes may contribute to dysregulated eating and other impulsive behaviors in binge-eating populations, but the best methods for reliably assessing the contributions of these processes to binge eating are unclear. In this review, we summarize standard neurocognitive and neuroimaging tasks that assess reward and control-related processes, describe adaptations of these tasks used to study eating and food-specific responsivity and deficits, and consider the advantages and limitations of these tasks. Future studies integrating both general and food-specific tasks with neuroimaging will improve understanding of the neurocognitive processes and neural circuits that contribute to binge eating and could inform novel interventions that more directly target or prevent this transdiagnostic behavior.

Elevated reward response to receipt of palatable food predicts future weight variability in healthy-weight adolescents.

Background: Both an elevated brain-reward-region response to palatable food and elevated weight variability have been shown to predict future weight gain.Objective: We examined whether the brain-reward response to food is related to future weight variability.Design: A total of 162 healthy-weight adolescents, who were aged 14-18 y at baseline, were enrolled in the study and were assessed annually over a 3-y follow-up period with 127 participants completing the final 3-y follow-up assessment. With the use of functional magnetic resonance imaging, we tested whether the neural responses to a cue that signaled an impending milkshake receipt and the receipt of the milkshake predicted weight variability over the follow-up period. Weight variability was modeled with a root mean squared error method to reflect fluctuations in weight independent of the net weight change.Results: Elevated activation in the medial prefrontal cortex and supplementary motor area, cingulate gyrus, cuneus and occipital gyrus, and insula in response to milkshake receipt predicted greater weight variability. Greater activation in the precuneus and middle temporal gyrus predicted lower weight variability.Conclusions: From our study data, we suggest that the elevated activation of reward and emotional-regulation brain regions (medial prefrontal cortex, cingulate cortex, and insula) and lower activation in self-reference regions (precuneus) in response to milkshake receipt predict weight variability over 3 y of follow-up. The fact that the reward response in the current study emerged in response to high-calorie palatable food receipt suggests that weight variability may be a measure of propensity periods of a positive energy balance and should be examined in addition to measures of the net weight change. With our collective results, we suggest that weight variability and its brain correlates should be added to other variables that are predictive of weight gain to inform the design of obesity-preventive programs in adolescents. This trial was registered at clinicaltrials.gov as NCT01807572.

Short-term variability in body weight predicts long-term weight gain.

BACKGROUND: Body weight in lower animals and humans is highly stable despite a very large flux in energy intake and expenditure over time. Conversely, the existence of higher-than-average variability in weight may indicate a disruption in the mechanisms responsible for homeostatic weight regulation. OBJECTIVE: In a sample chosen for weight-gain proneness, we evaluated whether weight variability over a 6-mo period predicted subsequent weight change from 6 to 24 mo. DESIGN: A total of 171 nonobese women were recruited to participate in this longitudinal study in which weight was measured 4 times over 24 mo. The initial 3 weights were used to calculate weight variability with the use of a root mean square error approach to assess fluctuations in weight independent of trajectory. Linear regression analysis was used to examine whether weight variability in the initial 6 mo predicted weight change 18 mo later. RESULTS: Greater weight variability significantly predicted amount of weight gained. This result was unchanged after control for baseline body mass index (BMI) and BMI change from baseline to 6 mo and for measures of disinhibition, restrained eating, and dieting. CONCLUSIONS: Elevated weight variability in young women may signal the degradation of body weight regulatory systems. In an obesogenic environment this may eventuate in accelerated weight gain, particularly in those with a genetic susceptibility toward overweight. Future research is needed to evaluate the reliability of weight variability as a predictor of future weight gain and the sources of its predictive effect. The trial on which this study is based is registered at clinicaltrials.gov as NCT00456131.